Advanced microgrid optimization using price-elastic demand response and greedy rat swarm optimization for economic...

Area of Science:

• Electrical Engineering
• Energy Systems
• Optimization Algorithms

Background:

• Microgrids require efficient energy management to integrate diverse energy resources.
• Price-based demand response programs (DRPs) offer a mechanism to influence consumer behavior and manage load.
• Optimization algorithms are crucial for scheduling distributed energy resources (DERs) effectively.

Purpose of the Study:

• To propose a comprehensive energy management framework for microgrids.
• To minimize generation costs and environmental impact by integrating DRPs and DERs.
• To evaluate the performance of the Greedy Rat Swarm Optimizer (GRSO) for microgrid optimization.

Main Methods:

• Development of four demand response models: exponential, hyperbolic, logarithmic, and critical peak pricing (CPP).
• Integration of a flexible elasticity matrix to model consumer response to price signals.
• Application of the Greedy Rat Swarm Optimizer (GRSO) for scheduling DERs (solar, wind, fossil fuels).
• Evaluation of four operational scenarios including grid participation and different pricing strategies (RTP, TOU, CPP).

Main Results:

• The GRSO achieved a minimum generation cost of 746¥ under critical peak pricing (CPP), a 15.4% reduction.
• Logarithmic demand response model reduced costs to 817¥ with limited grid participation.
• Significant peak load reduction observed, with load factor improvements up to 87.7%.
• Limiting grid upstream power capacity increased generation costs by 7%.

Conclusions:

• The proposed GRSO-based framework efficiently minimizes microgrid operational costs and environmental impact.
• Dynamic demand response strategies and grid participation are vital for cost-effective and sustainable microgrid management.
• The GRSO algorithm demonstrates superior performance in speed and convergence for real-time microgrid optimization.